Importing Data and Libraries
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## ✔ purrr 1.0.2
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library(plotly)##
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## layoutmnprescounty2012 <- read.csv("~/Downloads/mnprescounty2012.csv", header=FALSE)
mnprescounty2016 <- read.csv("~/Downloads/mnprescounty2016.csv", header=FALSE)
povertyincome2012 <- read.csv("~/Downloads/2012_povertyincome.csv", header=FALSE)
povertyincome2016 <- read.csv("~/Downloads/povertyincome_2016.csv", header=FALSE)Cleaning up dataframes
#Renaming Columns
Pres_2012 <- rename(mnprescounty2012, State = V1, County = V2, Office_ID = V4, Office_Name = V5, Cand_ID = V7, Cand_Name = V8, Party_ID = V11, Precincts_Reporting = V12, Total_Precincts = V13, Votes_For_Cand = V14, Perc_Votes_Cand_By_Office = V15, Total_Votes_For_Office = V16)
Pres_2016 <- rename(mnprescounty2016, State = V1, County = V2, Office_ID = V4, Office_Name = V5, Cand_ID = V7, Cand_Name = V8, Party_ID = V11, Precincts_Reporting = V12, Total_Precincts = V13, Votes_For_Cand = V14, Perc_Votes_Cand_By_Office = V15, Total_Votes_For_Office = V16)
#Removing NA Columns or Columns we don't need
Pres_Data_2012 <- select(Pres_2012,County, Office_ID,Cand_ID, Cand_Name, Precincts_Reporting, Total_Precincts, Votes_For_Cand, Total_Votes_For_Office)
Pres_Data_2016 <- select(Pres_2016,County, Office_ID,Cand_ID, Cand_Name, Precincts_Reporting, Total_Precincts, Votes_For_Cand, Total_Votes_For_Office)
#Converting County ID to factor so we can rename the levels for actual County name instead of number
Pres_Data_2012$County <- as.factor(Pres_Data_2012$County)
Pres_Data_2016$County <- as.factor(Pres_Data_2016$County)#More cleaning up of other dataframes
povinc_2012 <- rename(povertyincome2012, Poverty_All = V3, Med_Household_Inc = V21,County = V24)
filter_2012 <- povinc_2012 %>%
select(Poverty_All,Med_Household_Inc,County)
#removing first row as it contained irrelevant information
pov_inc_2012 <- filter_2012[-c(1), ]
data_2012 <- pov_inc_2012 %>%
mutate(County = str_remove_all(County, "County")) %>%
mutate(County = str_to_lower(County)) %>%
mutate(County = str_trim(County, side = c("both")))
#Doing the same cleaning for 2016 data
povinc_2016 <- rename(povertyincome2016,Poverty_All = V3,Med_Household_Inc = V21,County = V24)
filter_2016 <- povinc_2016 %>%
select(Poverty_All, Med_Household_Inc, County)
pov_inc_2016 <- filter_2016[-c(1), ]
data_2016 <- pov_inc_2016 %>%
mutate(County = str_remove_all(County, "County")) %>%
mutate(County = str_to_lower(County)) %>%
mutate(County = str_trim(County, side = c("both")))#renaming factor levels
presdata2012 <- Pres_Data_2012 %>%
mutate(County = fct_recode(County,
"Aitkin" = "1",
"Anoka" = "2",
"Becker" = "3",
"Beltrami" = "4",
"Benton" = "5",
"Big Stone" = "6",
"Blue Earth" = "7",
"Brown" = "8",
"Carlton" = "9",
"Carver" = "10",
"Cass" = "11",
"Chippewa" = "12",
"Chisago" = "13",
"Clay" = "14",
"Clearwater" = "15",
"Cook" = "16",
"Cottonwood" = "17",
"Crow Wing" = "18",
"Dakota" = "19",
"Dodge" = "20",
"Douglas" = "21",
"Faribault" = "22",
"Fillmore" = "23",
"Freeborn" = "24",
"Goodhue" = "25",
"Grant" = "26",
"Hennepin" = "27",
"Houston" = "28",
"Hubbard" = "29",
"Isanti" = "30",
"Itasca" = "31",
"Jackson" = "32",
"Kanabec" = "33",
"Kandiyohi" = "34",
"Kittson" = "35",
"Koochiching" = "36",
"Lac qui Parle" = "37",
"Lake" = "38",
"Lake of the Woods" = "39",
"Le Sueur" = "40",
"Lincoln" = "41",
"Lyon" = "42",
"McLeod" = "43",
"Mahnomen" = "44",
"Marshall" = "45",
"Martin" = "46",
"Meeker" = "47",
"Mille Lacs" = "48",
"Morrison" = "49",
"Mower" = "50",
"Murray" = "51",
"Nicollet" = "52",
"Nobles" = "53",
"Norman" = "54",
"Olmsted" = "55",
"Otter Tail" = "56",
"Pennington" = "57",
"Pine" = "58",
"Pipestone" = "59",
"Polk" = "60",
"Pope" = "61",
"Ramsey" = "62",
"Red Lake" = "63",
"Redwood" = "64",
"Renville" = "65",
"Rice" = "66",
"Rock" = "67",
"Roseau" = "68",
"Saint Louis" = "69",
"Scott" = "70",
"Sherburne" = "71",
"Sibley" = "72",
"Stearns" = "73",
"Steele" = "74",
"Stevens" = "75",
"Swift" = "76",
"Todd" = "77",
"Traverse" = "78",
"Wabasha" = "79",
"Wadena" = "80",
"Waseca" = "81",
"Washington" = "82",
"Watonwan" = "83",
"Wilkin" = "84",
"Winona" = "85",
"Wright" = "86",
"Yellow Medicine" = "87"
)) %>%
mutate(County = str_to_lower(County)) %>%
mutate(County = str_remove_all(County, "//s"))
presdata2016 <- Pres_Data_2016 %>%
mutate(County = fct_recode(County,
"Aitkin" = "1",
"Anoka" = "2",
"Becker" = "3",
"Beltrami" = "4",
"Benton" = "5",
"Big Stone" = "6",
"Blue Earth" = "7",
"Brown" = "8",
"Carlton" = "9",
"Carver" = "10",
"Cass" = "11",
"Chippewa" = "12",
"Chisago" = "13",
"Clay" = "14",
"Clearwater" = "15",
"Cook" = "16",
"Cottonwood" = "17",
"Crow Wing" = "18",
"Dakota" = "19",
"Dodge" = "20",
"Douglas" = "21",
"Faribault" = "22",
"Fillmore" = "23",
"Freeborn" = "24",
"Goodhue" = "25",
"Grant" = "26",
"Hennepin" = "27",
"Houston" = "28",
"Hubbard" = "29",
"Isanti" = "30",
"Itasca" = "31",
"Jackson" = "32",
"Kanabec" = "33",
"Kandiyohi" = "34",
"Kittson" = "35",
"Koochiching" = "36",
"Lac qui Parle" = "37",
"Lake" = "38",
"Lake of the Woods" = "39",
"Le Sueur" = "40",
"Lincoln" = "41",
"Lyon" = "42",
"McLeod" = "43",
"Mahnomen" = "44",
"Marshall" = "45",
"Martin" = "46",
"Meeker" = "47",
"Mille Lacs" = "48",
"Morrison" = "49",
"Mower" = "50",
"Murray" = "51",
"Nicollet" = "52",
"Nobles" = "53",
"Norman" = "54",
"Olmsted" = "55",
"Otter Tail" = "56",
"Pennington" = "57",
"Pine" = "58",
"Pipestone" = "59",
"Polk" = "60",
"Pope" = "61",
"Ramsey" = "62",
"Red Lake" = "63",
"Redwood" = "64",
"Renville" = "65",
"Rice" = "66",
"Rock" = "67",
"Roseau" = "68",
"Saint Louis" = "69",
"Scott" = "70",
"Sherburne" = "71",
"Sibley" = "72",
"Stearns" = "73",
"Steele" = "74",
"Stevens" = "75",
"Swift" = "76",
"Todd" = "77",
"Traverse" = "78",
"Wabasha" = "79",
"Wadena" = "80",
"Waseca" = "81",
"Washington" = "82",
"Watonwan" = "83",
"Wilkin" = "84",
"Winona" = "85",
"Wright" = "86",
"Yellow Medicine" = "87"
)) %>%
mutate(County = str_to_lower(County))#Making Interactive Map
#Could choose the county and find information based on what county you chose.
#Also a graph that shows some type of change over time and we make a inference from that?? If any states swung a certain way/any changes in poverty???#Joining Data
all_data_2012 <- full_join(presdata2012, data_2012)## Joining with `by = join_by(County)`all_data_2016 <- full_join(presdata2016, data_2016)## Joining with `by = join_by(County)`#Filtering candidates down to only republican/democrat main. tickets
new_2012 <- all_data_2012 %>%
filter(Cand_ID %in% c(401,301))
new_2016 <- all_data_2016 %>%
filter(Cand_ID %in% c(401,301))#Filtering data for there to be 1 row for each county; keeping the row with whichever candidate won that county
final_2012 <- new_2012 %>%
group_by(County) %>%
slice_max(order_by = Votes_For_Cand, n = 1, with_ties = FALSE) %>%
ungroup()
#Doing same thing for 2016 dataset
final_2016 <- new_2016 %>%
group_by(County) %>%
slice_max(order_by = Votes_For_Cand, n = 1, with_ties = FALSE) %>%
ungroup()library(shiny)
library(leaflet)
library(sf)## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUElibrary(ggplot2)
library(dplyr)
library(plotly)
library(patchwork)
county_df <- map_data("county")
counties <- county_df %>%
filter(region == "minnesota")
counties_new <- rename(counties, County = subregion)
counties_final <- counties_new %>%
select(long, lat, County, group) %>%
mutate(County = fct_recode(County,
"saint louis" = "st louis"
))#Joining long/lat data with previous datasets for each year to create interactive maps.
joined_2012 <- full_join(final_2012, counties_final)## Joining with `by = join_by(County)`joined_2012$Cand_Name <- as.factor(joined_2012$Cand_Name)
#Same for 2016 data
joined_2016 <- full_join(final_2016, counties_final)## Joining with `by = join_by(County)`#Making both plots for each year
mn_2012 <- ggplot(joined_2012, aes(x = long, y = lat, group = group )) +
geom_polygon(color = "black",aes(fill = Cand_Name, text = paste(
"County:",County, "<br>",
"Number of Individuals in Poverty:",Poverty_All, "<br>",
"Median Income:$",Med_Household_Inc, "<br>",
"Candidate: ", Cand_Name))) +
scale_fill_manual(values = c("#0015bc", "#ff0000")) +
theme(axis.title.x.bottom = element_blank(), axis.title.y.left = element_blank()) +
labs(title = "2012 Election Minnesota", legend = "Candidate Name", fill = "Candidate Names") +
theme_void()## Warning in geom_polygon(color = "black", aes(fill = Cand_Name, text =
## paste("County:", : Ignoring unknown aesthetics: textmn_2016 <- ggplot(joined_2016, aes(x = long, y = lat, group = group )) +
geom_polygon(color = "black",aes(fill = Cand_Name)) +
scale_fill_manual(values = c("#ff0000", "#0015bc")) +
theme(axis.title.x.bottom = element_blank(), axis.title.y.left = element_blank()) +
labs(title = "2016 Election Minnesota", legend = "Candidate Name", fill = "Candidate Names") +
theme_void()
ggplotly(mn_2012)